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EP2261816A3 - Split variational inference - Google Patents

Split variational inference Download PDF

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Publication number
EP2261816A3
EP2261816A3 EP10165368.1A EP10165368A EP2261816A3 EP 2261816 A3 EP2261816 A3 EP 2261816A3 EP 10165368 A EP10165368 A EP 10165368A EP 2261816 A3 EP2261816 A3 EP 2261816A3
Authority
EP
European Patent Office
Prior art keywords
region
interest
function
partitioning
estimating
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10165368.1A
Other languages
German (de)
French (fr)
Other versions
EP2261816A2 (en
Inventor
Guillaume Bouchard
Onno Zoeter
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xerox Corp
Original Assignee
Xerox Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xerox Corp filed Critical Xerox Corp
Publication of EP2261816A2 publication Critical patent/EP2261816A2/en
Publication of EP2261816A3 publication Critical patent/EP2261816A3/en
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Algebra (AREA)
  • Mathematical Optimization (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Probability & Statistics with Applications (AREA)
  • Pure & Applied Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Feedback Control In General (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

] A method comprises: partitioning a region of interest into a plurality of soft bin regions that span the region of interest; estimating an integral over each soft bin region of a function defined over the region of interest; and outputting a value equal to or derived from the sum of the estimated integrals over the soft bin regions spanning the region of interest. The method may further comprise: integrating a Bayesian theorem function using the partitioning, estimating, and outputting operations, and classifying an object to be classified using a classifier trained using the Bayesian machine learning. The method may further comprise performing optimal control by iteratively minimizing a controlled system cost function to determine optimized control inputs using the partitioning, estimating, and outputting with the function equal to the controlled system cost function having the selected control inputs, and controlling the controlled system using the optimized control inputs.
EP10165368.1A 2009-06-10 2010-06-09 Split variational inference Withdrawn EP2261816A3 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US12/481,802 US8190550B2 (en) 2009-06-10 2009-06-10 Split variational inference

Publications (2)

Publication Number Publication Date
EP2261816A2 EP2261816A2 (en) 2010-12-15
EP2261816A3 true EP2261816A3 (en) 2016-05-04

Family

ID=42782245

Family Applications (1)

Application Number Title Priority Date Filing Date
EP10165368.1A Withdrawn EP2261816A3 (en) 2009-06-10 2010-06-09 Split variational inference

Country Status (2)

Country Link
US (1) US8190550B2 (en)
EP (1) EP2261816A3 (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102859528A (en) 2010-05-19 2013-01-02 加利福尼亚大学董事会 Systems and methods for identifying drug targets using biological networks
CN103559294A (en) * 2013-11-12 2014-02-05 中国石油大学(北京) Construction method and device as well as sorting method and device for support vector machine sorter
US10235686B2 (en) 2014-10-30 2019-03-19 Microsoft Technology Licensing, Llc System forecasting and improvement using mean field
CN109829583B (en) * 2019-01-31 2022-10-11 成都思晗科技股份有限公司 Mountain fire risk prediction method based on probability programming technology
CN111198099A (en) * 2019-12-13 2020-05-26 南京松数科技有限公司 Fan health state monitoring method based on Bayesian data driving
WO2025104310A1 (en) * 2023-11-17 2025-05-22 Deepmind Technologies Limited High-performance and low-complexity neural compression from a single image, video or audio data

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
CHRISTOPHER M BISHOP ET AL: "Bayesian Hierarchical Mixtures of Experts", 19TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 7 August 2003 (2003-08-07), Acapulco, Mexico, XP055260930 *
GUILLAUME BOUCHARD ET AL: "Split variational inference", PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE ON MACHINE LEARNING, ICML '09, 14 June 2009 (2009-06-14), New York, New York, USA, pages 1 - 8, XP055261066, ISBN: 978-1-60558-516-1, DOI: 10.1145/1553374.1553382 *
H A CHIPMAN ET AL: "Discussion of the paper Bayesian Treed Generalized Linear Models", BAYESIAN STATISTICS 7: PROCEEDINGS OF THE SEVENTH VALENCIA INTERNATIONAL MEETING, 11 September 2003 (2003-09-11), pages 98 - 101, XP055260720, ISBN: 978-0-19-852615-5, Retrieved from the Internet <URL:http://research.microsoft.com/pubs/67168/bishop-valencia-03.pdf> [retrieved on 20160323] *
JUERGEN FRITSCH ET AL: "Adaptively Growing Hierarchical Mixtures of Experts", 1 January 1996 (1996-01-01), XP055260922, Retrieved from the Internet <URL:https://www-preview.ri.cmu.edu/pub_files/pub1/fritsch_j_rgen_1996_1/fritsch_j_rgen_1996_1.pdf> [retrieved on 20160324] *
S R WATERHOUSE ET AL: "Constructive Algorithms for Hierarchical Mixtures of Experts", ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 8, 1 January 1995 (1995-01-01), XP055261059, Retrieved from the Internet <URL:https://papers.nips.cc/paper/1165-constructive-algorithms-for-hierarchical-mixtures-of-experts.pdf> [retrieved on 20160324] *

Also Published As

Publication number Publication date
EP2261816A2 (en) 2010-12-15
US8190550B2 (en) 2012-05-29
US20100318490A1 (en) 2010-12-16

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